Medical imaging or intervention often relies on an estimation, or model, of a patient upon whom the imaging or intervention is to be performed. For example, position-dependent imaging parameters may differ depending upon the location of a patient's head and torso with respect to an imaging device. Accordingly, a model of the patient is determined prior to imaging in order to conform the imaging parameters to the patient anatomy. The model may include locations of anatomical landmarks, such as shoulders, pelvis, torso, knees, etc.
A model may be determined based on external and/or internal image data. Some conventional systems compare an acquired surface image of a patient against a library of pre-modeled surface images to determine a model corresponding to the patient. The determination may be performed by a neural network which is trained based on the library of pre-modeled surface images.
Conventional modeling techniques may accurately model one portion or segment of the body (e.g., a leg) while failing to accurately model other portions. Conventional techniques also encounter difficulty if a subject patient is disposed in a pose which is not adequately represented in the training library of pre-modeled surface images. What is needed is a system for efficient and suitably-accurate landmark detection based on efficiently-acquired images.